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Improved statistical machine translation using monolingual paraphrases ...
Nakov, Preslav. - : arXiv, 2021
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Slav-NER: the 3rd Cross-lingual Challenge on Recognition, Normalization, Classification, and Linking of Named Entities across Slavic languages ...
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Slav-NER: the 3rd Cross-lingual Challenge on Recognition, Normalization, Classification, and Linking of Named Entities across Slavic languages ...
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4
A Neighbourhood Framework for Resource-Lean Content Flagging ...
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5
Few-Shot Cross-Lingual Stance Detection with Sentiment-Based Pre-Training ...
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6
SemEval-2021 Task 6: Detection of Persuasion Techniques in Texts and Images ...
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Detecting Propaganda Techniques in Memes ...
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SOLID: A Large-Scale Semi-Supervised Dataset for Offensive Language Identification ...
Abstract: Read paper: https://www.aclanthology.org/2021.findings-acl.80 Abstract: The widespread use of offensive content in social media has led to an abundance of research in detecting language such as hate speech, cyberbullying, and cyber-aggression. Recent work presented the OLID dataset, which follows a taxonomy for offensive language identification that provides meaningful information for understanding the type and the target of offensive messages. However, it is limited in size and the OLID dataset might be biased towards offensive language as it was collected using keywords. In this work, we present SOLID, an expanded dataset using a more principled collection of tweets. SOLID contains over nine million English tweets labeled in a semi-supervised manner. We demonstrate that using SOLID along with OLID yields sizable performance gains on the OLID test set for two different models, especially for the lower levels of the taxonomy. ...
Keyword: Computational Linguistics; Condensed Matter Physics; Deep Learning; Electromagnetism; FOS Physical sciences; Information and Knowledge Engineering; Neural Network; Semantics
URL: https://dx.doi.org/10.48448/grg6-k740
https://underline.io/lecture/26171-solid-a-large-scale-semi-supervised-dataset-for-offensive-language-identification
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9
Detecting Harmful Memes and Their Targets ...
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10
SUper Team at SemEval-2016 Task 3: Building a feature-rich system for community question answering ...
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11
Sentiment Analysis in Twitter for Macedonian ...
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12
Feature-Rich Named Entity Recognition for Bulgarian Using Conditional Random Fields ...
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13
RuleBERT: Teaching Soft Rules to Pre-Trained Language Models ...
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14
SemEval-2020 Task 12: Multilingual Offensive Language Identification in Social Media (OffensEval 2020) ...
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15
On a Novel Application of Wasserstein-Procrustes for Unsupervised Cross-Lingual Learning ...
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16
EXAMS: A Multi-Subject High School Examinations Dataset for Cross-Lingual and Multilingual Question Answering ...
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17
SemEval-2020 Task 12: Multilingual Offensive Language Identification in Social Media (OffensEval 2020) ...
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18
SemEval-2020 Task 12: Multilingual Offensive Language Identification in Social Media (OffensEval 2020) ...
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19
What Was Written vs. Who Read It: News Media Profiling Using Text Analysis and Social Media Context ...
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20
SemEval-2020 Task 11: Detection of Propaganda Techniques in News Articles ...
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